Post on 30-Dec-2015
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Chapter 19
Transaction Management
Transparencies
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Chapter 19 - Objectives
Function and importance of transactions. Properties of transactions. Concurrency Control
– Meaning of serializability.– How locking can ensure serializability.– Deadlock and how it can be resolved.– How timestamping can ensure
serializability.– Optimistic concurrency control.– Granularity of locking.
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Chapter 19 - Objectives
Recovery Control– Some causes of database failure.– Purpose of transaction log file.– Purpose of checkpointing.– How to recover following database
failure. Alternative models for long duration transactions.
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Transaction Support
Transaction
Action, or series of actions, carried out by user or application, which accesses or changes contents of database.
Logical unit of work on the database. Application program is series of transactions with non-
database processing in between. Transforms database from one consistent state to
another, although consistency may be violated during transaction.
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Example Transaction
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Transaction Support
Can have one of two outcomes:– Success - transaction commits and database reaches a
new consistent state.
– Failure - transaction aborts, and database must be restored to consistent state before it started.
– Such a transaction is rolled back or undone.
Committed transaction cannot be aborted. Aborted transaction that is rolled back can be
restarted later.
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State Transition Diagram for Transaction
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Properties of Transactions
Four basic (ACID) properties of a transaction are:
Atomicity ‘All or nothing’ property.
Consistency Must transform database from one consistent state to another.
Isolation Partial effects of incomplete transactions should not be visible to other transactions.
Durability Effects of a committed transaction are permanent and must not be lost because of later failure.
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DBMS Transaction Subsystem
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Concurrency Control
Process of managing simultaneous operations on the database without having them interfere with one another.
Prevents interference when two or more users are accessing database simultaneously and at least one is updating data.
Although two transactions may be correct in themselves, interleaving of operations may produce an incorrect result.
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Need for Concurrency Control
Three examples of potential problems caused by concurrency: – Lost update problem.– Uncommitted dependency problem.– Inconsistent analysis problem.
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Lost Update Problem
Successfully completed update is overridden by another user.
T1 withdrawing £10 from an account with balx, initially £100.
T2 depositing £100 into same account. Serially, final balance would be £190.
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Lost Update Problem
Loss of T2’s update avoided by preventing T1 from reading balx until after update.
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Uncommitted Dependency Problem
Occurs when one transaction can see intermediate results of another transaction before it has committed.
T4 updates balx to £200 but it aborts, so balx should be back at original value of £100.
T3 has read new value of balx (£200) and uses value as basis of £10 reduction, giving a new balance of £190, instead of £90.
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Uncommitted Dependency Problem
Problem avoided by preventing T3 from reading balx until after T4 commits or aborts.
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Inconsistent Analysis Problem
Occurs when transaction reads several values but second transaction updates some of them during execution of first.
Sometimes referred to as dirty read or unrepeatable read.
T6 is totaling balances of account x (£100), account y (£50), and account z (£25).
Meantime, T5 has transferred £10 from balx to balz, so T6 now has wrong result (£10 too high).
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Inconsistent Analysis Problem
Problem avoided by preventing T6 from reading balx and balz until after T5 completed updates.
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Serializability
Objective of a concurrency control protocol is to schedule transactions in such a way as to avoid any interference( 冲突 ).
Could run transactions serially, but this limits degree of concurrency or parallelism in system.
Serializability identifies those executions of transactions guaranteed to ensure consistency.
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Serializability
ScheduleSequence of reads/writes by set of concurrent transactions.
Serial Schedule( 串行调度 )Schedule where operations of each transaction are executed consecutively without any interleaved ( 交 叉 的 ) operations from other transactions.
No guarantee that results of all serial executions of a given set of transactions will be identical( 同一的 ).
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Nonserial Schedule( 非串行调度 )
Schedule where operations from set of concurrent transactions are interleaved.
Objective of serializability is to find nonserial schedules that allow transactions to execute concurrently without interfering with one another.
In other words, want to find nonserial schedules that are equivalent to some serial schedule. Such a schedule is called serializable.
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Serializability
In serializability, ordering of read/writes is important:
(a) If two transactions only read a data item, they do not conflict and order is not important.
(b) If two transactions either read or write completely separate data items, they do not conflict and order is not important.
(c) If one transaction writes a data item and another reads or writes same data item, order of execution is important.
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Example of Conflict Serializability
Equivalent schedule: (a) nonserial schedule Sa, (b) noserial schedule Sb equivalent to Sa, (c) serial schedule Sc equivalent to Sa and Sb
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Serializability
Conflict serializable schedule orders any conflicting operations in same way as some serial execution.
Under constrained write rule (transaction updates data item based on its old value, which is first read), use precedence graph( 先后顺序图 ) to test for serializability.
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Precedence Graph
Create:– node for each transaction;
– a directed edge Ti Tj, if Tj reads the value of an item written by Ti;
– a directed edge Ti Tj, if Tj writes a value into an item after it has been read by Ti.
If precedence graph contains cycle, schedule is not conflict serializable.
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Example - Non-conflict serializable schedule
T9 is transferring £100 from one account with balance balx to another account with balance baly.
T10 is increasing balance of these two accounts by 10%.
Precedence graph has a cycle and so is not serializable.
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Example - Non-conflict serializable schedule
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View Serializability
Offers less stringent( 严 厉 的 ) definition of schedule equivalence than conflict serializability.
Two schedules S1 and S2 are view equivalent if:– For each data item x, if Ti reads initial value of x in S1, Ti
must also read initial value of x in S2.– For each read on x by Ti in S1, if value read by x is
written by Tj, Ti must also read value of x produced by Tj in S2.
– For each data item x, if last write on x performed by Ti in S1, same transaction must perform final write on x in S2.
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View Serializability
Schedule is view serializable if it is view equivalent to a serial schedule.
Every conflict serializable schedule is view serializable, although converse is not true.
It can be shown that any view serializable schedule that is not conflict serializable contains one or more blind writes.
In general, testing whether schedule is serializable is NP-complete.
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Example - View Serializable schedule
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Recoverability( 可恢复性 )
Serializability identifies schedules that maintain database consistency, assuming no transaction fails.
Could also examine recoverability of transactions within schedule.
If transaction fails, atomicity requires effects of transaction to be undone.
Durability states that once transaction commits, its changes cannot be undone (without running another, compensating( 补偿 ), transaction).
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Recoverable Schedule( 可恢复调度 )
A schedule where, for each pair of transactions Ti and Tj, if Tj reads a data item previously written by Ti, then the commit operation of Ti precedes the commit operation of Tj.
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Concurrency Control Techniques
Two basic concurrency control techniques:– Locking,– Timestamping.
Both are conservative( 保 守 的 ) approaches: delay transactions in case they conflict with other transactions.
Optimistic( 乐观的 ) methods assume conflict is rare and only check for conflicts at commit.
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Locking
Transaction uses locks to deny access to other transactions and so prevent incorrect updates.
Most widely used approach to ensure serializability. Generally, a transaction must claim a shared (read)
or exclusive (write) lock on a data item before read or write.
Lock prevents another transaction from modifying item or even reading it, in the case of a write lock.
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Locking - Basic Rules
If transaction has shared lock on item, can read but not update item.
If transaction has exclusive lock on item, can both read and update item.
Reads cannot conflict, so more than one transaction can hold shared locks simultaneously on same item.
Exclusive lock gives transaction exclusive access to that item.
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Locking - Basic Rules
Some systems allow transaction to upgrade read lock to an exclusive lock, or downgrade exclusive lock to a shared lock.
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Example - Non-conflict serializable schedule
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Example - Incorrect Locking Schedule
For two transactions above, a valid schedule using these rules is:
S = {write_lock(T9, balx), read(T9, balx), write(T9, balx), unlock(T9, balx), write_lock(T10, balx), read(T10, balx), write(T10, balx), unlock(T10, balx), write_lock(T10, baly), read(T10, baly), write(T10, baly), unlock(T10, baly), commit(T10), write_lock(T9, baly), read(T9, baly), write(T9, baly), unlock(T9, baly), commit(T9) }
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Example - Incorrect Locking Schedule
If at start, balx = 100, baly = 400, result should be:
balx = 220, baly = 330, if T9 executes before T10, or
balx = 210, baly = 340, if T10 executes before T9.
However, result gives balx = 220 and baly = 340.
S is not a serializable schedule.
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Example - Incorrect Locking Schedule
Problem is that transactions release locks too soon, resulting in loss of total isolation and atomicity.
To guarantee serializability, need an additional protocol concerning the positioning of lock and unlock operations in every transaction.
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Two-Phase Locking (2PL)
Transaction follows 2PL protocol if all locking operations( 所 有 加 锁 操 作 ) precede first unlock operation in the transaction.
Two phases for transaction:– Growing phase( 增 长 阶 段 ) - acquires all
locks but cannot release any locks.– Shrinking phase( 收缩阶段 ) - releases locks
but cannot acquire any new locks.
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Preventing Lost Update Problem using 2PL
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Preventing Uncommitted Dependency Problem using 2PL
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Preventing Inconsistent Analysis Problem using 2PL
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Cascading Rollback( 级联回滚 )
If every transaction in a schedule follows 2PL, schedule is serializable.
However, problems can occur with interpretation of when locks can be released.
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Cascading Rollback
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Cascading Rollback
Transactions conform to 2PL. T14 aborts. Since T15 is dependent on T14, T15 must also be
rolled back. Since T16 is dependent on T15, it too must be rolled back.
This is called cascading rollback. To prevent this with 2PL, leave release of all locks
until end of transaction (Rigorous 2PL ). Hold exclusive locks until the end of the
transaction (Strict 2PL ).
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Concurrency Control with Index Structures
Could treat each page of index as a data item and apply 2PL.
However, as indexes will be frequently accessed, particularly higher levels, this may lead to high lock contention( 锁争夺 ).
Can make two observations about index traversal:– Search path starts from root and moves down to leaf
nodes but search never moves back up tree. Thus, once a lower-level node has been accessed, higher-level nodes in that path will not be used again.
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Concurrency Control with Index Structures
– When new index value (key and pointer) is being inserted into a leaf node, then if node is not full, insertion will not cause changes to higher-level nodes.
Suggests only have to exclusively lock leaf node in such a case, and only exclusively lock higher-level nodes if node is full and has to be split.
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Concurrency Control with Index Structures
Thus, can derive following locking strategy:– For searches, obtain shared locks on nodes starting at root
and proceeding downwards along required path. Release lock on node once lock has been obtained on the child node.
– For insertions, conservative approach would be to obtain exclusive locks on all nodes as we descend tree to the leaf node to be modified.
– For more optimistic approach, obtain shared locks on all nodes as we descend to leaf node to be modified, where obtain exclusive lock. If leaf node has to split, upgrade shared lock on parent to exclusive lock. If this node also has to split, continue to upgrade locks at next higher level.
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Deadlock
An impasse that may result when two (or more) transactions are each waiting for locks held by the other to be released.
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Deadlock
Only one way to break deadlock: abort one or more of the transactions.
Deadlock should be transparent to user, so DBMS should restart transaction(s).
Three general techniques for handling deadlock: – Timeouts.– Deadlock prevention.– Deadlock detection and recovery.
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Timeouts
Transaction that requests lock will only wait for a system-defined period of time.
If lock has not been granted within this period, lock request times out.
In this case, DBMS assumes transaction may be deadlocked, even though it may not be, and it aborts and automatically restarts the transaction.
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Deadlock Prevention
DBMS looks ahead to see if transaction would cause deadlock and never allows deadlock to occur.
Could order transactions using transaction timestamps:– Wait-Die - only an older transaction can
wait for younger one, otherwise transaction is aborted (dies) and restarted with same timestamp.
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Deadlock Prevention
– Wound-Wait - only a younger transaction can wait for an older one. If older transaction requests lock held by younger one, younger one is aborted (wounded).
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Deadlock Detection and Recovery
DBMS allows deadlock to occur but recognizes it and breaks it.
Usually handled by construction of wait-for graph (WFG) showing transaction dependencies:– Create a node for each transaction.– Create edge Ti -> Tj, if Ti waiting to lock item
locked by Tj. Deadlock exists if and only if WFG contains
cycle. WFG is created at regular intervals.
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Example - Wait-For-Graph (WFG)
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Recovery from Deadlock Detection
Several issues:– choice of deadlock victim;– how far to roll a transaction back. It may be
possible to resolve the deadlock by rolling part of the transaction ;
– avoiding starvation( 饿 死 ). Starvation occurs when the same transaction is always chosen as victim, and the transaction can never complete.
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Timestamping( 时间戳 )
Transactions ordered globally so that older transactions, transactions with smaller timestamps, get priority in the event of conflict.
Conflict is resolved by rolling back and restarting transaction.
No locks so no deadlock.
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Timestamping
Timestamp
A unique identifier created by DBMS that indicates relative starting time of a transaction.
Can be generated by using system clock at time
transaction started, or by incrementing a logical counter every time a new transaction starts.
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Timestamping
Read/write proceeds only if last update on that data item was carried out by an older transaction.
Otherwise, transaction requesting read/write is restarted and given a new timestamp.
Also timestamps for data items:– read-timestamp - timestamp of last transaction
to read item;– write-timestamp - timestamp of last
transaction to write item.
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Timestamping - Read(x)
Consider a transaction T with timestamp ts(T):
若事务 T 请求 read(x)(a). ts(T) < write_timestamp(x)
– x already updated by younger (later) transaction.– Transaction must be aborted and restarted with a new
(later) timestamp.
(b). ts(T) >= write_timestamp(x), -- operation is accepted and executed. -- and read_timestamp(x) = max(ts(T), read_timestamp(x)).
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Timestamping - Read(x)
若事务 T 请求 write(x)
(a). ts(T) < read_timestamp(x)
– x already read by younger transaction. This means that a later transaction is already using current value of the item and it would be an error to update it now.
– Roll back transaction and restart it using a later timestamp.
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Timestamping - Write(x)
ts(T) < write_timestamp(x)
– x already written by younger transaction.– Write of T can safely be ignored - ignore
obsolete write rule ( 忽略废弃写规则 ).
Otherwise, operation is accepted and executed.
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Example – Basic Timestamp Ordering
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Comparison of Methods
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Optimistic Techniques ( 乐观技术 )
Based on assumption that conflict is rare and more efficient to let transactions proceed without delays to ensure serializability.
At commit, check is made to determine whether conflict has occurred.
If there is a conflict, transaction must be rolled back and restarted.
Potentially allows greater concurrency than traditional protocols.
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Optimistic Techniques
Three phases:
– Read– Validation– Write
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Optimistic Techniques - Read Phase
Extends from start until immediately before commit.
Transaction reads values from database and stores them in local variables. Updates are applied to a local copy of the data.
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Optimistic Techniques - Validation Phase
Follows the read phase. For read-only transaction, checks that data read
are still current values. If no interference, transaction is committed, else aborted and restarted.
For update transaction, checks transaction leaves database in a consistent state, with serializability maintained.
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Optimistic Techniques - Write Phase
Follows successful validation phase for update transactions.
Updates made to local copy are applied to the database.
冲突少时，乐观技术非常高效，但会造成个别事务回滚，若回滚的事务执行时间长，则造成较大时间浪费。
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Granularity( 粒度 ) of Data Items
Size of data items chosen as unit of protection by concurrency control protocol.
Ranging from coarse( 粗 ) to fine( 细 ):– The entire database.– A file.– A page (or area or database spaced).– A record.– A field value of a record.
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Granularity of Data Items
Tradeoff: – coarser, the lower the degree of concurrency; – finer, more locking information that is needed
to be stored. Best item size depends on the types of
transactions. 加锁的粒度越粗，死锁的可能性也越大。 动态数据项大小技术，可以在当事务对文件中
某个百分数的记录或页面进行封锁时，自动将封锁粒度提高到文件。
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Hierarchy of Granularity ( 粒度层次 )
Could represent granularity of locks in a hierarchical structure.
Root node represents entire database, level 1s represent files, etc.
When node is locked, all its descendants( 后代 ) are also locked.
DBMS should check hierarchical path before granting lock.
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Hierarchy of Granularity
Intention lock could be used to lock all ancestors of a locked node.
Intention locks can be read or write. IS 锁 ( 意向共享锁 ): 如果对某个数据对象加 IS 锁 , 表
示它的后代节点拟 ( 意向 ) 加 S 锁 , 例 : 要对某个元组加S 锁 , 则先要对关系和数据库加 IS 锁 ;
IX 锁 ( 意向排它锁 ): 要对某个元组加 X 锁 , 则先要对关系和数据库加 IX 锁 ;
SIX 锁 ( 共享意向排它锁 ): 如果对某个表加 SIX 锁 , 则表示该事务要对读整个表 ( 加 S 锁 ), 同时会更新个别元组 .
Applied top-down, released bottom-up. 父节点被意向锁封锁，其子节点才可以被封锁 ; 所有子节点都被解锁，该父节点才可以被解锁 .
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Hierarchy of Granularity
X
SIX
S IX
IS
锁的强度偏序关系锁的强度是指对其他锁的排斥程度。一个事务在申请加锁时，以强锁代替弱锁是安全的，反之不然。
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Levels of Locking
具有意向锁的多粒度封锁方法提高了系统的并发性 ,减少了加锁和解锁的开销 .
ORACLE数据库系统采用了这种封锁方法 .
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Database Recovery
Process of restoring database to a correct state in the event of a failure.
Need for Recovery Control– Two types of storage: volatile (main memory) and
nonvolatile （非易失性的） .
– Volatile storage does not survive system crashes.
– Stable storage represents information that has been replicated in several nonvolatile storage media with independent failure modes.
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Types of Failures
System crashes, resulting in loss of main memory.
Media failures, resulting in loss of parts of secondary storage.
Application software errors. Natural physical disasters. Carelessness or unintentional destruction of
data or facilities. Sabotage （蓄意破坏） .
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Transactions and Recovery
Transactions represent basic unit of recovery. Recovery manager responsible for atomicity and
durability. If failure occurs between commit and database
buffers being flushed to secondary storage then, to ensure durability, recovery manager has to redo (rollforward) transaction’s updates.
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Transactions and Recovery
If transaction had not committed at failure time, recovery manager has to undo (rollback) any effects of that transaction for atomicity.
Partial undo - only one transaction has to be undone.
Global undo - all transactions have to be undone.
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Example
DBMS starts at time t0, but fails at time tf. Assume data for transactions T2 and T3 have been written to secondary storage.
T1 and T6 have to be undone. In absence of any other information, recovery manager has to redo T2, T3, T4, and T5.
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Recovery Facilities （恢复机制） DBMS should provide following facilities to assist
with recovery:
– Backup mechanism, which makes periodic backup copies of database.
– Logging facilities, which keep track of current state of transactions and database changes.
– Checkpoint facility, which enables updates to database in progress to be made permanent.
– Recovery manager, which allows DBMS to restore database to consistent state following a failure.
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Log File
Contains information about all updates to database:– Transaction records.– Checkpoint records.
Often used for other purposes (for example, monitoring or auditing ，如用户登录、注销等 ).
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Log File
Transaction records contain:– Transaction identifier.– Type of log record, (transaction start, insert,
update, delete, abort, commit).– Identifier of data item affected by database
action (insert, delete, and update operations).– Before-image ( 前像） of data item.– After-image （后像） of data item.– Log management information.
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Sample Log File
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Log File
Log file may be duplexed or triplexed. Log file sometimes split into two separate
random-access files. Potential bottleneck; critical in determining
overall performance.
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Checkpointing
CheckpointPoint of synchronization （同步点） between database and log file. All buffers are force-written to secondary storage.
Checkpoint record is created containing identifiers of all active transactions.
When failure occurs, redo all transactions that committed since the checkpoint and undo all transactions active at time of crash.
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Checkpointing
In previous example, with checkpoint at time tc, changes made by T2 and T3 have been written to secondary storage.
Thus:
– only redo T4 and T5,
– undo transactions T1 and T6.
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Recovery Techniques
If database has been damaged:– Need to restore last backup copy of database and
reapply updates of committed transactions using log file.
If database is only inconsistent:– Need to undo changes that caused inconsistency.
May also need to redo some transactions to ensure updates reach secondary storage.
– Do not need backup, but can restore database using before- and after-images in the log file.
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Main Recovery Techniques
Three main recovery techniques:
– Deferred Update ( 延迟更新）– Immediate Update– Shadow Paging （镜像页技术）
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Deferred Update
Updates are not written to the database until after a transaction has reached its commit point.
If transaction fails before commit, it will not have modified database and so no undoing of changes required.
May be necessary to redo updates of committed transactions as their effect may not have reached database.
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Immediate Update
Updates are applied to database as they occur. Need to redo updates of committed transactions
following a failure. May need to undo effects of transactions that
had not committed at time of failure. Essential that log records are written before
write to database. Write-ahead log protocol.
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Immediate Update
If no “transaction commit” record in log, then that transaction was active at failure and must be undone.
Undo operations are performed in reverse order in which they were written to log.
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Shadow Paging
Maintain two page tables during life of a transaction: current page and shadow page table.
When transaction starts, two pages are the same. Shadow page table is never changed thereafter
and is used to restore database in event of failure. During transaction, current page table records
all updates to database. When transaction completes, current page table
becomes shadow page table.
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Advanced Transaction Models
Protocols considered so far are suitable for types of transactions that arise in traditional business applications, characterized by:– Data has many types, each with small number
of instances.– Designs may be very large.– Design is not static but evolves through time. – Updates are far-reaching.– Cooperative engineering.
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Advanced Transaction Models
May result in transactions of long duration, giving rise to following problems:– More susceptible to failure - need to minimize
amount of work lost.– May access large number of data items -
concurrency limited if data inaccessible for long periods.
– Deadlock more likely.– Cooperation through use of shared data items
restricted by traditional concurrency protocols.
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Advanced Transaction Models
Look at five advanced transaction models:
– Nested Transaction Model– Sagas– Multi-level Transaction Model– Dynamic Restructuring– Workflow Models
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Nested Transaction Model
Transaction viewed as hierarchy of subtransactions. Top-level transaction can have number of child
transactions. Each child can also have nested transactions. In Moss’s proposal, only leaf-level subtransactions
allowed to perform database operations. Transactions have to commit from bottom upwards. However, transaction abort at one level does not have
to affect transaction in progress at higher level.
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Nested Transaction Model
Parent allowed to perform its own recovery:– Retry subtransaction.– Ignore failure, in which case subtransaction
non-vital.– Run contingency subtransaction.– Abort.
Updates of committed subtransactions at intermediate levels are visible only within scope of their immediate parents.
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Nested Transaction Model
Further, commit of subtransaction is conditionally subject to commit or abort of its superiors.
Using this model, top-level transactions conform to traditional ACID properties of flat transaction.
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Example of Nested Transactions
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Nested Transaction Model - Advantages
Modularity - transaction can be decomposed into number of subtransactions for purposes of concurrency and recovery.
Finer level of granularity for concurrency control and recovery.
Intra-transaction parallelism. Intra-transaction recovery control.
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Emulating Nested Transactions using Savepoints
An identifiable point in flat transaction representing some partially consistent state.
Can be used as restart point for transaction if subsequent problem detected.
During execution of transaction, user can establish savepoint, which user can use to roll transaction back to.
Unlike nested transactions, savepoints do not support any form of intra-transaction parallelism.
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Sagas
“A sequence of (flat) transactions that can be interleaved with other transactions”.
DBMS guarantees that either all transactions in saga are successfully completed or compensating transactions are run to undo partial execution.
Saga has only one level of nesting. For every subtransaction defined, there is
corresponding compensating transaction that will semantically undo subtransaction’s effect.
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Sagas
Relax property of isolation by allowing saga to reveal its partial results to other concurrently executing transactions before it completes.
Useful when subtransactions are relatively independent and compensating transactions can be produced.
May be difficult sometimes to define compensating transaction in advance, and DBMS may need to interact with user to determine compensation.
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Multi-level Transaction Model
Closed nested transaction - atomicity enforced at the top-level.
Open nested transactions - allow partial results of subtransactions to be seen outside transaction.
Saga model is example of open nested transaction. So is multi-level transaction model where tree of
subtransactions is balanced. Nodes at same depth of tree correspond to
operations of same level of abstraction in DBMS.
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Multi-level Transaction Model
Edges represent implementation of an operation by sequence of operations at next lower level.
Traditional flat transaction ensures no conflicts at lowest level (L0).
In multi-level model two operations at level Li may not conflict even though their implementations at next lower level Li-1 do.
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Example - Multi-level Transaction Model
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Example - Multi-level Transaction Model
T7: T71, which increases balx by 5
T72, which subtracts 5 from baly
T8: T81, which increases baly by 10
T82, which subtracts 2 from balx
As addition and subtraction commute, can execute these subtransactions in any order, and correct result will always be generated.
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Dynamic Restructuring
To address constraints imposed by ACID properties of flat transactions, two new operations proposed: split_transaction and join_transaction.
split-transaction - splits transaction into two serializable transactions and divides its actions and resources (for example, locked data items) between new transactions.
Resulting transactions proceed independently.
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Dynamic Restructuring
Allows partial results of transaction to be shared, while still preserving its semantics.
Can be applied only when it is possible to generate two transactions that are serializable with each other and with all other concurrently executing transactions.
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Dynamic Restructuring
Conditions that permit transaction to be split into A and B are:– .AWriteSet BWriteSet BWriteLast.
If both A and B write to same object, B’s write operations must follow A’s write operations.
– .AReadSet BWriteSet = . A cannot see any results from B.
– .BReadSet AWriteSet = ShareSet. B may see results of A.
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Dynamic Restructuring
These guarantee that A is serialized before B. However, if A aborts, B must also abort. If both BWriteLast and ShareSet are empty,
then A and B can be serialized in any order and both can be committed independently.
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Dynamic Restructuring
join-transaction - performs reverse operation, merging ongoing work of two or more independent transactions, as though they had always been single transaction.
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Dynamic Restructuring
Main advantages of dynamic restructuring are:
Adaptive recovery. Reducing isolation.
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Workflow Models
Has been argued that above models are still not powerful to model some business activities.
More complex models have been proposed that are combinations of open and nested transactions.
However, as they hardly conform to any of ACID properties, called workflow model used instead.
Workflow is activity involving coordinated execution of multiple tasks performed by different processing entities (people or software systems).
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Workflow Models
Two general problems involved in workflow systems: – specification of the workflow, – execution of the workflow.
Both problems complicated by fact that many organizations use multiple, independently managed systems to automate different parts of the process.